This invention generally relates to pricing commodities, and more specifically, to pricing commodities based on a distributed forecasting by the users of their expected use of the commodities.
An important problem for the producers of a commodity is to forecast the future demand for the commodity. An accurate forecast allows the producer to optimize the amount of the commodity to produce, thereby minimizing the production cost or waste and maximizing revenue from sales.
For example, a utility company produces electricity to accommodate a large number of users. The challenge in forecasting use is that the demand of each user is very uncertain and hard to predict. On the one hand, accurate forecasting is computationally hard, especially when there are millions of users or meters. On the other hand, inaccurate forecasts lead to wastes and inefficiencies, as, among other reasons, excess electricity cannot be readily or efficiently stored.
Better forecasts would allow the utilities to determine an optimum amount of power and how to produce that power, thereby minimizing production costs and environmental impact, while also maximizing the utility's revenue. For instance, by knowing the peak power demand, utilities can use generators that pollute less, and the utilities can even integrate more renewable sources of power, such as wind, solar and others, into the power production process.
The current approach taken by most utility companies is that the producer of the power forecasts the demand, decides the amount of power to produce, and sets the price. However, this approach has a number of drawbacks. These drawbacks include forecasting demand is very difficult, and inaccurate forecasts often lead to volatile prices and inefficient production. Further, as mentioned above, forecasting is computationally hard, especially when there are millions of individual users. In addition, with current approaches, typically there is no input in the forecasting from the users of the power.